多机多卡训练SD系列模型（三）_sd模型 --resolution设置

多机多卡训练SD系列模型（三）

前言

dreambooth和直接微调的区别：秋叶说的）

Dreambooth训练Lora

和训练普通的lora方式一样，就是增加了正则化数据集

kohya-trainer的config解读

dataset_config

config = {
    "general": {
        "enable_bucket": True,
        "caption_extension": caption_extension,
        "shuffle_caption": True,
        "keep_tokens": keep_tokens,
        "bucket_reso_steps": 64,
        "bucket_no_upscale": False,
    },
    "datasets": [
        {
            "resolution": resolution,
            "min_bucket_reso": 320 if resolution > 640 else 256,  #最小分桶
            "max_bucket_reso": 1280 if resolution > 640 else 1024, #最大分桶
            "caption_dropout_rate": 0,    #描述词丢弃
            "caption_tag_dropout_rate": 0,
            "caption_dropout_every_n_epochs": 0,
            "flip_aug": flip_aug,  # 开启反转
            "color_aug": False,  #颜色增强
            "face_crop_aug_range": None,  #面部增强
            "subsets": subsets,  
        }
    ],
}
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enable_bucket开启分桶，这也算是这个脚本比较方便的地方
bucket_reso_steps分桶步数
新的训练脚本可以不进行数据剪裁，因为批量的数据剪裁通常都会降低画质，影响训练效果，手动剪裁又有些麻烦，就可以开启分桶，他会将大小基本一致的放入一个桶中丢去训练，

类似这样（这是四卡训练，所以会报四次），会让训练的时候更加好，lora训练使用了unet

分桶就能够让他在第一步中更好去进行，具体可以去看青龙在b站的lora底层原理。

这里可能会有个坑，就是在用webui打标的时候

第一个保持原始尺寸如果不开启，就会按照上面设置的长宽进行剪裁，出来的图就会奇奇怪怪，并且分桶都直接丢到一个桶里。

flip_aug开启水平翻转，开启后可以人为增加一倍的数据，但是对于有明显分边特征的建议不要开启，不然就会紊乱，比如左边脸有颗痣，开启后生成的图就会左一下右一下，包括刘海和大小眼。

config = {
    "model_arguments": {
        "v2": v2,
        "v_parameterization": v_parameterization if v2 and v_parameterization else False,
        "pretrained_model_name_or_path": pretrained_model_name_or_path,
        "vae": vae,
    },
    "additional_network_arguments": {
        "no_metadata": False,
        "unet_lr": float(unet_lr) if train_unet else None,
        "text_encoder_lr": float(text_encoder_lr) if train_text_encoder else None,
        "network_weights": network_weight,
        "network_module": network_module,
        "network_dim": network_dim,
        "network_alpha": network_alpha,
        "network_args": network_args,
        "network_train_unet_only": True if train_unet and not train_text_encoder else False,
        "network_train_text_encoder_only": True if train_text_encoder and not train_unet else False,
        "training_comment": None,
    },
    "optimizer_arguments": {
        "min_snr_gamma": min_snr_gamma if not min_snr_gamma == -1 else None,
        "optimizer_type": optimizer_type,
        "learning_rate": unet_lr,
        "max_grad_norm": 1.0,
        "optimizer_args": eval(optimizer_args) if optimizer_args else None,
        "lr_scheduler": lr_scheduler,
        "lr_warmup_steps": lr_warmup_steps,
        "lr_scheduler_num_cycles": lr_scheduler_num_cycles if lr_scheduler == "cosine_with_restarts" else None,
        "lr_scheduler_power": lr_scheduler_power if lr_scheduler == "polynomial" else None,
    },
    "dataset_arguments": {
        "cache_latents": True,
        "debug_dataset": False,
        "vae_batch_size": vae_batch_size,
    },
    "training_arguments": {
        "output_dir": output_dir,
        "output_name": project_name,
        "save_precision": save_precision,
        "save_every_n_epochs": save_n_epochs_type_value if save_n_epochs_type == "save_every_n_epochs" else None,
        "save_n_epoch_ratio": save_n_epochs_type_value if save_n_epochs_type == "save_n_epoch_ratio" else None,
        "save_last_n_epochs": None,
        "save_state": None,
        "save_last_n_epochs_state": None,
        "resume": None,
        "train_batch_size": train_batch_size,
        "max_token_length": 225,
        "mem_eff_attn": False,
        "xformers": True,
        "max_train_epochs": num_epochs,
        "max_data_loader_n_workers": 8,
        "persistent_data_loader_workers": True,
        "seed": seed if seed > 0 else None,
        "gradient_checkpointing": gradient_checkpointing,
        "gradient_accumulation_steps": gradient_accumulation_steps,
        "mixed_precision": mixed_precision,
        "clip_skip": clip_skip if not v2 else None,
        "logging_dir": logging_dir,
        "log_prefix": project_name,
        "noise_offset": noise_offset if noise_offset > 0 else None,
        "lowram": lowram,
    },
    "sample_prompt_arguments": {
        "sample_every_n_steps": None,
        "sample_every_n_epochs": 1 if enable_sample_prompt else 999999,
        "sample_sampler": sampler,
    },
    "dreambooth_arguments": {
        "prior_loss_weight": 1.0,
    },
    "saving_arguments": {
        "save_model_as": save_model_as
    },
}
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具体参数大同小异，主要的区别有dreambooth_arguments中的prior_loss_weight,是正则化数据集的初始loss值，越高正则化数据集对训练的污染越小，正常0.8-1.5左右。
sample_prompt_arguments是用来验证模型的，就是训练过程中通过一个设定好的prompt周期性让模型生成图片，但是实际用起来比较抽象可以不启用

accelerate_conf = {
    "config_file" : accelerate_config,
    "num_cpu_threads_per_process" : 1,
}

train_conf = {
    "sample_prompts" : sample_prompt, 
    "dataset_config" : dataset_config,
    "config_file" : config_file
}
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accelerate_config是accelerate库的配置，可以先前以固定格式导入，也可以运行 accelerate config来进行配置可以后面增加参数来指定配置文件生成路径，train_conf 中的就是上述几个配置文件的路径，配置文件可以统一写成toml的格式，例如

 [model_arguments]
pretrained_model_name_or_path = ""

[additional_network_arguments]
network_module = "networks.lora"
network_dim = 128
network_alpha = 64

[optimizer_arguments]
optimizer_type = ""
unet_lr=
text_encoder_lr = 
lr_warmup_steps = 
max_grad_norm = 
lr_scheduler_num_cycles = 
lr_scheduler = ""
[dataset_arguments]
cache_latents = 

[training_arguments]
output_dir = ""
output_name = ""
save_precision = "fp32"
save_every_n_epochs = 
train_batch_size = 
max_token_length = 
xformers = 
max_train_epochs = 
max_data_loader_n_workers = 
persistent_data_loader_workers = 
clip_skip = 
logging_dir = 
lowram = 
noise_offset = 

[dreambooth_arguments]
prior_loss_weight = 

[saving_arguments]
save_model_as = "safetensors"
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和

[general]
enable_bucket = true
shuffle_caption = false
keep_tokens = 1
[[datasets]]
flip_aug = false
batch_size = 6
resolution = 512
[[datasets.subsets]]
 image_dir = ""
 caption_extension = ".txt"
 num_repeats = 45
 class_tokens = ""
[[datasets.subsets]]
 image_dir = ""
 caption_extension = ""
 num_repeats = 
 class_tokens = "
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accelerate_config后续再讲

启动训练

可以写一个train.sh的脚本，然后运行就好了
可以用python run your_train.py也可以用accelerate执行，示例代码：

accelerate launch --config_file="./config/accelerate_config.yaml" train_network.py \
   --dataset_config="./config/dataset_config.toml" \
   --config_file="./config/config_file.toml"\
   --sample_prompts="./config/sample_prompt.txt"
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然后运行./train.sh就可以开始运行了

总结

主要写了kohya-trainer的训练脚本的使用，中间具体参数需要慢慢摸索，并且需要根据不同机器来运行不同的加速配置。